System and method for providing argument maps based on activity in a network environment

ABSTRACT

A method is provided in one example and includes receiving network traffic associated with a first user and a second user; evaluating keywords in the network traffic in order to identify a topic of discussion involving the first and the second users; determining a first sentiment associated with a first data segment associated with the first user; determining a second sentiment associated with a second data segment associated with the second user; and generating an argument map based on the first data sentiment and the second data sentiment.

TECHNICAL FIELD

This disclosure relates in general to the field of communications and,more particularly, to providing argument maps based on activity in anetwork environment.

BACKGROUND

The field of communications has become increasingly important in today'ssociety. In particular, the ability to effectively gather, associate,and organize information presents a significant obstacle for componentmanufacturers, system designers, and network operators. Argument mapsare generally difficult to develop, as an individual typically has tomanually input and attempt to summarize information associated with arecent information exchange. Commonly, argument maps fail to includedifferent mediums of communication. As new communication platforms andtechnologies become available, new protocols should be developed inorder to optimize the use of these emerging protocols.

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present disclosure andfeatures and advantages thereof, reference is made to the followingdescription, taken in conjunction with the accompanying figures, whereinlike reference numerals represent like parts, in which:

FIG. 1A is a simplified block diagram of a communication system forproviding argument maps based on network activity in accordance with oneembodiment;

FIG. 1B is a simplified flowchart illustrating one possible activityassociated with providing argument maps in accordance with oneembodiment;

FIG. 1C is a simplified schematic illustrating example statements and anargument mapping associated with one embodiment of the presentdisclosure;

FIG. 1D is a simplified schematic diagram of speech-to-text operationsthat can be performed in the communication system in accordance with oneembodiment;

FIG. 1E is a simplified block diagram of a media tagging module in thecommunication system in accordance with one embodiment;

FIG. 2 is a simplified block diagram of a central engine in thecommunication system in accordance with one embodiment;

FIG. 3 is a simplified flowchart illustrating a series of example stepsassociated with the communication system; and

FIG. 4 is a simplified flowchart illustrating another series of examplesteps associated with the communication system.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

A method is provided in one example and includes receiving networktraffic associated with a first user and a second user; evaluatingkeywords in the network traffic in order to identify a topic ofdiscussion involving the first and the second users; determining a firstsentiment associated with a first data segment associated with the firstuser; determining a second sentiment associated with a second datasegment associated with the second user; and generating an argument mapbased on the first data sentiment and the second data sentiment.

In other embodiments, the method can include converting a portion of thenetwork traffic from a speech format to a text format in order todetermine the first sentiment and the second sentiment. The argument mapcan be delivered to the first user and the second user as a result of adiscussion terminating between the first user and the second user. Thenetwork traffic and include email data, media data, video data, audiodata, etc. The email header information and signatures can be removedbefore determining the first sentiment and the second sentiment. In morespecific instances, timestamps and Internet Protocol (IP) addressesassociated with the first user and the second user are identified.Furthermore, an interaction associated with the topic of discussion ischronologically ordered before determining the first sentiment and thesecond sentiment.

Example Embodiments

FIG. 1A is a simplified block diagram of a communication system 10 fordeveloping intelligent argument maps based on network data. FIG. 1A mayinclude an end user 12, who is operating a computer device that isconfigured to interface with an Internet Protocol (IP) network 14. Inaddition, an administrator 20 is provided, where administrator 20 hasthe ability to interface with the architecture through an IP network 18.Communication system 10 may further include a network collaborationplatform (NCP) 32, which includes an add to whitelist/blacklist module34, a feedback loop module 36, and an administrator suggest interface38. Network Sensor 54 also includes a sentiment analyzer module 51 and asummarization module 53, which in this particular example areprovisioned along with text extraction module 58. Alternatively, thesemodules may be provisioned elsewhere based on particular configurationneeds. FIG. 1A may also include a central engine 40, which includes alightweight directory access protocol (LDAP) feeder element 42, avocabulary feeder module 44, an emerging vocabulary topics element 46,and a table write service element 48.

FIG. 1A may also include a network sensor 54 that includes a first in,first out (FIFO) element 56, a media tagging module 52, a textextraction module 58, a blacklist 60, a document type filter 62, a nounphrase extractor module 64, a whitelist 66, a document splitter element68, and a clean topics module 70. Multiple network sensors 54 may beprovisioned at various places within the network and such provisioningmay be based on how much information is sought to be tagged, thecapacity of various network elements, etc.

In accordance with certain embodiments, communication system 10 cancreate clarity and foster progress by developing argument mapsassociated with network activity. The argument map can be provided as agraphical representation, as a text document, as a diagram (inclusive ofvideo images), or provided in any other suitable format (e.g., renderedon a user interface). Communication system 10 is configured tosystematically review speech, text, media, etc. in order to developargument maps that effectively summarize interactions amongstindividuals. For example, there could be an email exchange on an emaillist (e.g., a particular business unit) amongst individuals. Thoseemails can be analyzed for keywords, sentiment, etc. in order to developan appropriate argument map that effectively summarizes the discussions.More specifically, communication system 10 can identify the topic ofdiscussion, the keywords used in the discussion, and the resultantopinions of those discussions (e.g., for/against a particular concept).The keywords can be suitably mapped back to each individual user tofurther develop their personal vocabulary.

Logistically, sentiment analyzer module 51 is configured to trackcertain words in order to determine sentiment. For example, the phrase“I don't like” can be readily identified as a negative sentiment.Similarly, the phrase “I really enjoyed” can be tagged as a positivesentiment. Additionally, there are degrees of sentiment such that thephrase “I absolutely hate” can be readily identified as a strongnegative sentiment, whereas the phrase “I truly love” can be identifiedas a strong positive sentiment. As used herein in this Specification,the term ‘sentiment’ is meant to include any type of feeling, opinion,statement, reaction, or expression associated with a given topic.

Before turning to additional operational capabilities of communicationsystem 10, certain foundational information is provided in order toelucidate some of the problematic areas associated with argument mapactivities. An argument map is a visual representation of the structureof an argument in informal logic. The argument map can include thecomponents of an argument such as main contentions, premises,co-premises, objections, rebuttals, and lemmas. Typically, an argumentmap is a “box and arrow” diagram with boxes corresponding topropositions, and arrows corresponding to relationships (e.g.,evidential support). Argument mapping is often designed to supportdeliberation over issues, ideas, and arguments. Furthermore, argumentmaps can support the analysis of pros and cons when deliberating overdifficult business problems.

Current argument mapping is entirely dependent on user inputs and theformat of these inputs. Manual argument mapping is time-consuming, andit has consistently proved to be inaccurate and/or biased. In employmentscenarios, workers attend numerous meetings, conduct discussions withtheir colleagues, and constantly write emails to each other. Hence,there are various forms of communication mediums in which individualsparticipate. In this context, it is difficult to track the primary topicand, further, to focus on the crucial points associated with the givenproblem. Argument maps improve critical thinking by providing clarity ofthought (visual displays), uncover hidden assumptions, and effectivelyevaluate strong and weak links.

Communication system 10 is configured to capture various forms ofcommunication mediums (as data segment inputs) in order to efficientlyprovide argument maps. As part of this processing, the platform canidentify the primary topic of discussion, the participants, thesentiment, and generate a suitable resultant (e.g., a discreteconclusion, an argument map, etc.). Note that to effectively addressface-to-face and virtual meetings (e.g., Telepresence, WebEx, etc.)communication system 10 can leverage speech-to-text conversiontechnology, along with video tagging.

Semantically, once the appropriate data has been captured from variousforms of corporate communication, basic and complex text analysis can beexecuted. For example, a semantic analysis, noun-phrase extractionanalysis, or a verb-phrase extraction can enable the platform toshortlist potential topics of discussion. In one particular example,occurrence counters can be used (potentially provided along with smartweights) to better identify the most probable topic being evaluated.Furthermore, the sentiment analysis can include classifying the polarityof a data segment in a given document, a sentence, an expressed opinion(which may be provided in audio, video, media) as positive, negative, orneutral. Further, these sentiments can have varying degrees such as‘strong negative’, ‘strong positive’, etc. The generated argument mapscan be suitably displayed to a user interface (e.g., when the a textanalysis detects a conclusion in the conversation).

Note that communication system 10 eliminates the cumbersome task ofmanual entry for generating argument maps. The platform develops theargument maps based on incoming data, where the process can routinelyoccur while individuals are participating in their daily employmentactivities. Additionally, any potential bias is eliminated in formingthe argument map, as there is no human intervention in generating themap. Separately, the argument mapping is predictable, produced quickly,and readily available to any interested/authorized party.

Turning to FIG. 1B, FIG. 1B is a simplified flowchart 400 illustratingone potential example associated with communication system 10. Thisparticular operation may begin at 402, where input is received fromindividuals who may be operating in a network environment. Note thatcommunication system 10 is not confined to a network environment;however, most employment scenarios involve tracking emails, documents,video conferences, audio calls, etc., where all of these possibilitiescan be included as being part of a network environment. At 404, the rootconversation is derived, which may be based on timestamps. This activitycould include deriving the origin of dialogue between any number ofindividuals (e.g., identifying their respective IP addresses).

At 406, the conversation can be sorted in chronological order, while atstep 408, email header and signature cleaning can be performed. Notethat if the system is dealing with a speech transcript, this step may beskipped entirely. At 410, dialogue entities are extracted, wheresentiment scores can also be developed. At 412, objects can beaggregated in order to derive a primary topic of discussion. At 414,summarization activities can occur in order to extract the parts ofdata/speech, which have the top entities embedded in them. At 416, acategorization occurs for the derived statements with respect to theargument map attributes (e.g., the main contention, premises,co-premises, objections, rebuttals, and lemmas). Part of theseactivities may involve speech recognition, sentiment analyses, basiclinguistic models, etc. At 418, the argument map is suitably generatedand provided to any suitable/authorized individual.

FIG. 1C is a simplified schematic diagram 77 illustrating an exampleargument map developed and processed by communication system 10. In thisparticular example, six statements are used to develop an appropriateargument map. The six statements are provided as: 1) Jim is part of thevoice technology group and cannot attend the meeting; 2) Jessica worksin the voice technology group, and she is stuck in traffic; 3) Morgan isin the voice technology group; 4) Morgan is on vacation this week; 5) Idid not invite any other voice technology group members to the meeting;6) [Conclusion] No voice technology group members will attend themeeting. Note that such a conclusion can be provided in a text format,an audio format, a video format, an email format, or in any othersuitable format, which may be based on particular preferences orconfiguration needs.

Consider another example in which an email discussion can be used toderive automated argument maps to enable logic and clarity in thearguments. Step one would involve deriving the root email and thenordering the emails chronologically. The second step would involveextracting the email subject/title, which in this particular example isprovided as “A Good Read.” The third step involves performing any typeof email header and signature cleaning (i.e., removal). The fourth stepinvolves identifying entities, and the appropriate sentiment for eachemail document. The entities in such a case can be associated withpeople, places, products, companies, or any other appropriate object.The sentiment can be produced as a numerical value that aligns with sometype of formulated scale. For example, a sentiment between 0.4-0.6represents a positive sentiment, whereas anything above 0.6 wouldrepresent a strong positive sentiment for that particular statement,document, email, etc. Similarly, any sentiment between −0.1 and +0.1 canbe regarded as neutral.

Subsequently, the entity counts can be tallied, where the data would besuitably aggregated in order to identify the primary topic ofdiscussion. Note that there could be secondary topics of discussion thatare similarly identified. Once this is completed, the summarization maybe performed in order to derive a set of statements. Summarization canbe based on extracting sentences that include the top two entities inthis particular example. After this is completed, the argumentstatements may be derived, which may include the components of anargument such as main contentions, premises, co-premises, objections,rebuttals, and lemmas. The final step would include generating asuitable argument map, which may also provide a suitable conclusion(s)for any interested party to review. Note that default settings incertain network scenarios can involve systematically developing theseargument maps once a given session (e.g., video session, audio session,Telepresence session, WebEx session, email session) has finished.

Turning to the infrastructure of FIG. 1A, IP networks 14 and 18represent a series of points or nodes of interconnected communicationpaths for receiving and transmitting packets of information, whichpropagate through communication system 10. IP networks 14 and 18 offer acommunicative interface between servers (and/or end users) and may beany local area network (LAN), a wireless LAN (WLAN), a metropolitan areanetwork (MAN), a virtual LAN (VLAN), a virtual private network (VPN), awide area network (WAN), or any other appropriate architecture or systemthat facilitates communications in a network environment. IP networks 14and 18 can implement a TCP/IP communication language protocol in aparticular embodiment of the present disclosure; however, IP networks 14and 18 may alternatively implement any other suitable communicationprotocol for transmitting and receiving data packets withincommunication system 10.

Note that the elements of FIG. 1A-1B can readily be part of a server incertain embodiments of this architecture. In one example implementation,network sensor 54, central engine 40, and/or NCP 32 are (or are part of)network elements that facilitate or otherwise helps coordinate theargument map operations, as explained herein. As used herein in thisSpecification, the term ‘network element’ is meant to encompass networkappliances, servers, routers, switches, gateways, bridges,loadbalancers, firewalls, processors, modules, or any other suitabledevice, proprietary component, element, or object operable to exchangeinformation in a network environment. Moreover, the network elements mayinclude any suitable hardware, software, components, modules,interfaces, or objects that facilitate the operations thereof. This maybe inclusive of appropriate algorithms and communication protocols thatallow for the effective exchange of data or information. Note that eachof network sensor 54, central engine 40, and/or NCP 32 can beprovisioned with their own dedicated processors and memory elements (notshown), or alternatively the processors and memory elements may beshared by network sensor 54, central engine 40, and NCP 32.

In one example implementation, network sensor 54 includes software(e.g., as part of text extraction module 58, sentiment analyzer module51, summarization module 53, etc.) to achieve the argument mapoperations, as outlined herein in this document. In other embodiments,this feature may be provided externally to any of the aforementionedelements, or included in some other network device to achieve thisintended functionality. Alternatively, several elements may includesoftware (or reciprocating software) that can coordinate in order toachieve the operations, as outlined herein. In still other embodiments,any of the devices of FIG. 1A may include any suitable algorithms,hardware, software, components, modules, interfaces, or objects thatfacilitate these argument building operations. Additional operationalcapabilities of communication system 10 are detailed below.

Turning to the formulation of the personal vocabulary, it should benoted that in generating a large corpus of vocabulary words, one issuearises due to false positives. For example, the words “meet” and “meat”shared the same phonetics. If an architecture fails to account for thesesignificant phonetics, then data collection, processing, and searchingcan ultimately be inaccurate. For example, when a new search isperformed on each of these terms identified above, both terms couldyield a large number of results. However, if the person who issued thequery is interested in finding information (or people) related to sometype of ‘meet’-ing protocol (e.g., in the context of WebEx technology),then those search results are targeting a specific usage of the term“meet.” Results that are returned and that include the term “meat” areirrelevant for this particular search. Additionally, the person whoreceived the inaccurate results is forced to sort through theseirrelevant terms.

Communication system 10 can offer an intelligent filtering of words byleveraging the personal vocabulary of the individual who is associatedwith the collected data. The personal vocabulary can be developed in adifferent workflow, where the elimination of false positives representsan application of that personal vocabulary against an incoming mediafile. For example, as the system processes new end user media files(e.g., video, audio, any combination of audio/video, etc.), anadditional layer of filtering can be performed that checks the collected(or tagged) terms against personal vocabulary. Thus, if a particular enduser has a personal vocabulary that includes the term “meet”, then asmedia files are identifying phonetically accurate words (e.g., “meet”,“meat”) in the audio track, the extraneous term (i.e., “meat”) would beeliminated as being a false positive. Note that the probability of apersonal vocabulary having two words that phonetically sound the same islow. This factor can be used in order to remove a number of falsepositives from information that is collected and sought to be tagged.This engenders a higher quality of phoneme-based speech recognition.Hence, the personal vocabulary can be used to increase the accuracy ofterms tagged in media file scenarios.

In one general sense, an application can be written on top of theformation of an intelligent personal vocabulary database. A partitionedpersonal vocabulary database can be leveraged in order to furtherenhance accuracy associated with incoming media files (subject totagging) to remove false positives that occur in the incoming data.Thus, the media tagging activity is making use of the personalvocabulary (which is systematically developed), to refine phonemetagging.

The personal vocabulary developed by communication system 10 can be usedto augment the tagging results associated with video or audio files.Phoneme technology breaks down speech (for example, from analog todigital, voice segmenting, etc.) in order to provide text, which isbased on the media file. For example, as a video file enters into thesystem, the objective is to capture relevant enterprise terms to bestored in some appropriate location. The repository that stores thisresultant data can be searched for terms based on a search query.Phonetic based audio technology offers a mechanism that is amenable toaudio mining activities. A phonetic-index can be created for every audiofile that is to be mined. Searches can readily be performed on thesephonetic indices, where the search terms could be free form.

In one example, an end user can upload a video file onto the system.Enterprise vocabulary can be tagged for this particular video file(e.g., using various audio-to-text operations). The resulting enterprisevocabulary can be confirmed based on that particular end user's personalvocabulary, which has already been amassed. For example, if an originaltagging operation generated 100 tags for the uploaded video file, byapplying the personal vocabulary check, the resulting tags may bereduced to 60 tags. These resulting 60 tags are more accurate, moresignificant, and reflect the removal of false positives from thecollection of words. Additional details related to media tagging module52 are provided below with reference to the FIGURES. Before turning tothose details, some primary information is offered related to how theunderlying personal vocabulary is constructed and developed.

Communication system 10 can intelligently harvest network data from avariety of end users, and automatically create personal vocabulary frombusiness vocabulary by observing each user's interaction/traffic on thenetwork. In a general sense, the architecture can isolate terms perperson in order to define an end user's personal vocabulary. Thisinformation can subsequently be used to identify specific experts. Inother instances, the personal vocabulary can be used for topic-basedsocial graph building (e.g., social networking applications). In otherinstances, this information can be used to improve the accuracy ofspeech-to-text translations, which can relate to the individualapplications being used by the person, a particular environment in whichthe end user participates, feature invocation applications, etc. Thesolution can intelligently and dynamically auto generate different listsof personal vocabulary per user without creating additional overhead forthe end users.

As part of its personal vocabulary development activities, communicationsystem 10 can tag words for specific end users. For example, relevantwords identified in an enterprise system can be extracted from thedocuments, which are flowing through the network. The tags can becategorized and then associated to the user, who generated or whoconsumed each document. In accordance with one example implementation, atag can be given different weights depending on several potentialdocument characteristics. One characteristic relates to the type ofdocument propagating in the network (for example, email, an HTTPtransaction, a PDF, a Word document, a text message, an instant message,etc.). Another characteristic relates to the type of usage beingexhibited by the end user. For example, the system can evaluate if theend user represents the producer of the content (e.g., the sender, theposter, etc.), or the consumer of the content (e.g., the recipient, theaudience member, etc.). In one example, if the end user were posting adocument including the identified vocabulary, the act of posting suchwords would accord the words a higher weight, than merely receiving anemail that includes the particular vocabulary words. Stated in differentterms, in a forum in which the end user is authoring a document to beposted (e.g., on a blog, on a corporate website, in a corporateengineering forum, etc.), vocabulary words within that document wouldhave a higher associative value than if the words were propagating inlesser forums (e.g., a passive recipient in an email forum). Yet anothercharacteristic relates to a probability of a term showing up in adocument. (Note that multiple word terms have a lower probability ofoccurrence and, therefore, carry a higher weight when they areidentified). In one instance, the tagged vocabulary words can beaggregated using streaming databases, where the aggregated tags can bestored and archived in a summarized format.

The resulting information may be suitably categorized in any appropriateformat. For example, a dynamic database (e.g., table, list, etc.) can begenerated for each individual user, each user-to-user communication(e.g., 1-1, N or N, etc.), and each type of document (e.g., email, phoneconversation messages, Meeting Place meeting data, WebEx data, blogposting, White Paper, PDF, Word document, video file, audio file, textmessage, etc.). Essentially, any type of information propagating in thenetwork can be suitably categorized in the corresponding database of thetendered architecture. Some of the possible database configurations aredescribed below with reference to FIG. 2.

It should be noted that there are several different types of objectsflowing through the architecture of communication system 10. Componentswithin communication system 10 can identify which objects should beprocessed by particular components of the configuration. One set ofobjects relates to media files. These can be received by FIFO element 56and subsequently passed to media tagging module 52. The resultants (fromprocessing, which occurs at media tagging module 52) is then passed totext extraction module 58.

In operation of an example that is illustrative of business vocabularybeing developed, at vocabulary feeder module 44, data can be sent bynoun phrase extractor module 64, (i.e., the content field) and this canbe used for vocabulary suggestion for administrator 20. This data can beanonymous, having no user concept. For LDAP feeder element 42,whitelisted terms are provided and, further, this can be used forpersonal vocabulary building, as discussed herein. In essence, this databelongs to a particular user; it is a document associated to a user.Thus, there are two distinct workflows occurring in the architecture,which processes different types of documents for different purposes.

For the business vocabulary workflow, one aspect of the architectureinvolves a noun phrase extraction component, which can be provided alongwith filtering mechanisms, and stream access counts to retrieve popularand/or new vocabulary terms. In one example implementation, involvingthe development of business vocabulary, the architecture can suggestwords and phrases that are potential vocabulary candidates. Multi-wordphrases can be given more weight than single word terms. The decisionwhether to include these words in the whitelist or the blacklist canrest with the vocabulary administrator. The administrator can alsodecide if the words should never be brought to his attention again bymarking them for addition to the list of administrator stop words. Thiscan take the form of a feedback loop, for example, from the NCP userinterface to the network sensor/central engine (depending on where thestop word removal component may reside).

In one example embodiment, only a certain domain of data (e.g., words)of vocabulary is tagged. As used herein in this Specification, the term‘data’ is meant to encompass any information (video, text, audio,multimedia, voice, etc.) in any suitable format that propagates in anetwork environment. The particular domain could be provided in awhitelist, which reflects specific network content. In one exampleimplementation, administrator 20 can develop a certain domain thatrespects privacy issues, privileged content, etc. such that the ultimatecomposite of documents or files would reflect information capable ofbeing shared amongst employees in a corporate (potentially public)environment. In certain implementations, the resultant composite ofdocuments (i.e., data) can help to identify experts associated withspecific subject matter areas; however, there are a myriad of additionaluses to which communication system 10 can apply. As used herein in thisSpecification, the term ‘resultant composite’ can be any object,location, database, repository, server, file, table, etc. that can offeradministrator 20 the results generated by communication system 10.

Turning to FIG. 1D, FIG. 1D is a simplified schematic diagramillustrating a number of speech-to-text operations 30 that may occurwithin communication system 10. FIG. 1D includes a waveform acquisitionelement 31, a waveform segmenting element 33, a phoneme matching element35, and a text generation element 37. The speech-to-text conversion caninclude a number of stages. For example, the waveform acquisition cansample the analog audio waveform. The waveform segmentation can breakthe waveform into individual phonemes (e.g., eliminating laughter,coughing, various background noises, etc.). Phoneme matching can assigna symbolic representation to the phoneme waveform (e.g., using some typeof phonetic alphabet). In addition, the text generation can map phonemesto their intended textual representation (e.g., using the term “meet” or“meat”). If more than one mapping is possible (as in this example), acontextual analysis can be used to choose the most likely version.

In operation, media tagging module 52 can be configured to receive amedia file (video, audio, etc.) and transform that information into atext tagged file, which is further passed to a document indexingfunction. More specifically, and in one example implementation, there isa separate workflow that occurs before text extraction activities areperformed. This separate workflow can address media files, which requiresome type of conversion from audio to text. For example, if a video filewere to be received, audio information would be identified and,subsequently, converted to text information to identify relevantenterprise vocabulary. An audio stream can be converted to a phoneticindex file (i.e., a phonetic audio track). Once the phonetic index fileis created, an enterprise vocabulary can be applied to search forenterprise terms within this phonetic index file. In one instance, theenterprise vocabulary may include one or more whitelist words, which canbe developed or otherwise configured (e.g., by an administrator).

Applying the enterprise vocabulary can include, for example, taking eachword within the enterprise vocabulary and searching for those particularwords (e.g., individually) in the audio track. For example, for anenterprise vocabulary of 1000 words, a series of application programinterfaces (APIs) can be used to identify that a given word (“meet”) isfound at specific time intervals (T=3 seconds, T=14 seconds, T=49seconds, etc.). The resultant could be provided as a list of 40 words(in this particular example).

This list can be checked against a personal vocabulary database, whichis particular to the end user who is seeking to send, receive, upload,etc. this media file. Thus, the personal vocabulary (e.g., having 250words) can be loaded and leveraged in order to eliminate false positiveswithin the 40 words. This could further reduce the resultant list to 25words. A resulting text file can be fed to text extraction module 58 foradditional processing, as outlined herein.

FIG. 1E is a simplified block diagram that illustrates additionaldetails relating to an example implementation of media tagging module52. Media tagging module 52 may include a video-to-audio converter 72, aphoneme engine 74, a tagged file 76, a thumbnail module 92, a memoryelement 94, a processor 96, and a personal vocabulary database 78. A rawvideo file 82 can be sought to be uploaded by end user 12, and it canpropagate through media tagging module 52 in order to generate taggeddata with false positives removed 84. Additionally, a search module 98is also provided in FIG. 1E and this element can interact with mediatagging module 52 in order to search information that has already beenintelligently filtered using the various mechanisms outlined herein. Forexample, a search interface could be provided (to a given end user) andthe interface could be configured to initiate a search for particularsubject areas within a given database. The removal of false positivescan occur at an indexing time such that when an end user provides a newsearch to the system, the database is more accurate and, therefore, abetter search result is retrieved.

In the context of one example flow, media can be extracted from HTTPstreams, where it is subsequently converted to audio information. Theaudio track can be phonetic audio track (PAT) indexed. Appropriate tagscan be generated and indexed, where thumbnails are transported andsaved. Queries can be then served to the resulting database of entries(e.g., displayed as thumbnails), where relevant video and audio filescan be searched. Duplicate video entries can be removed, modified,edited, etc. on a periodic basis (e.g., by an administrator, or by someother individual). In addition, the appropriate video or audio playercan offer a suitable index (e.g., provided as a “jump-to” feature) thataccompanies the media.

Speech recognition can be employed in various media contexts (e.g.,video files, Telepresence conferences, phone voicemails, dictation,etc.). In addition, any number of formats can be supported bycommunication system 10 such as flash video (FLV), MPEG, MP4, MP3, WMV,audio video interleaved (AVI), MOV, Quick Time (QT) VCD, MP4, DVD, etc.Thumbnail module 92 can store one or more thumbnails on a platform thatconnects individual end users. The platform could be (for example) usedin the context of searching for particular types of informationcollected by the system. Note that any of the video and media tagging,speech recognition, etc. can be incorporated into the data segmentanalysis for generating suitable argument maps. Any of the processingactivities discussed herein can readily cooperate with the argument mapdevelopment.

Turning to technical details related to how the personal vocabulary isdeveloped, FIG. 2 is a simplified block diagram of an exampleimplementation of central engine 40. Central engine 40 includes a memoryelement 86 and a processor 88 in this particular configuration. Centralengine 40 also includes a junk filter mechanism 47 (which may be taskedwith removing erroneous vocabulary items), a vocabulary module 49, aweighting module 55, a streaming database feeder 50, a MQC 59, a CQC 61,a topics database 63, a collaboration database 65, an indexer module 67,and an index database 69. Indexer module 67 is configured to assist incategorizing the words (and/or noun phrases) collected in communicationsystem 10. Those indices can be stored in index database 69, which canbe searched by a given administrator or an end user. Along similarreasoning, topics database 63 can store words associated with particulartopics identified within the personal vocabulary. Collaboration database65 can involve multiple end users (e.g., along with administrator 20) informulating or refining the aggregated personal vocabulary words and/ornoun phrases. In regards to vocabulary module 49, this storage area canstore the resultant composite of vocabulary words (e.g., perindividual), or such information can be stored in any of the otherdatabases depicted in FIG. 2. It is imperative to note that this exampleof FIG. 2 is merely representing one of many possible configurationsthat central engine 40 could have. Other permutations are clearly withinthe broad scope of the tendered disclosure.

In operation of a simplified example used for discussion purposes, theextraction and processing operations can be performed on network sensor54, where those results may be provided to central engine 40 forbuilding personal vocabulary. With respect to the initial text strippingoperations, noun phrase extractor module 64 can find the noun phrases inany text field. In more specific implementations, pronouns and singlewords are excluded from being noun phrases. A noun phrase can be part ofa sentence that refers to a person, a place, or a thing. In mostsentences, the subject and the object (if there is one) are nounphrases. Minimally, a noun phrase can consist of a noun (e.g., “water”or “pets”) or a pronoun (e.g., “we” or “you”). Longer noun phrases canalso contain determiners (e.g., “every dog”), adjectives (e.g., “greenapples”) or other preceding, adjectival nouns (e.g., “computer monitorrepair manual”), and other kinds of words, as well. They are called nounphrases because the headword (i.e., the word that the rest of thephrase, if any, modifies) is a noun or a pronoun. For search and otherlanguage applications, noun phrase extraction is useful because much ofthe interesting information in text is carried by noun phrases. Also,most search queries are noun phrases. Thus, knowing the location of thenoun phrases within documents and, further, extracting them can be animportant step for tagging applications.

For the end-user interface, periodically, terms can be suggested to theadministrator for adding to the vocabulary. The existing interface foruser-suggested vocabulary could be used for displaying the terms to theadministrator. In one example implementation, a stop word removalfeature can be provided on central engine 40 (e.g., this could makeimplementation of the feedback loop more efficient). In other instances,the stop word removal feature is placed on network sensor 54 so thatonly the filtered fields are sent over to central engine 40. The conceptfield can be accessible like other fields in the received/collecteddocuments. The concept field is a list of string field values.Additional functionalities associated with these operations are bestunderstood in the context of several examples provided below.

While this is occurring, in a separate workflow personal vocabulary canbe developed. Thus, communication system 10 can generate personalvocabulary using corporate vocabulary, which is propagating in thenetwork. In practical terms, it is difficult to tag all user traffic ina corporate (i.e., enterprise) environment. There are two modes in whichcorporate vocabulary can be generated. First, in a learning mode, whereend users are not yet subscribed, automatic corporate vocabulary can begenerated by tagging content as it flows through the network. This canbe generated by tagging content anonymously in the network. Thistypically happens in the learning mode of the system, where no users aresubscribed on the system. The user whose content is being tagged is notnecessarily of interest at the time of corporate vocabulary generation.Second, in a real-time system scenario, as users begin using the system,users have the ability to suggest new words to the corporate vocabularythrough a manual process, feedback loops, etc., which are detailedherein.

By contrast, personal vocabulary generation can use corporate vocabularyto tag words for particular users. As documents (e.g.,email/http/videos, PDF, etc.) flow through the network, the systemchecks for words from the corporate vocabulary, tags the appropriatewords (e.g., using a whitelist), and then associates those words withparticular users. Communication system 10 can include a set of rules anda set of algorithms that decide whether tagged words should be added toa personal vocabulary. Rules include common term threshold, groupvocabulary adjustment, etc. Over a period, the user's personalvocabulary develops into a viable representation of subject areas (e.g.categories) for this particular end user. In addition, the user has theability to add words to his personal vocabulary manually. He also hasthe ability to mark individual words as public or private, where thelatter would prohibit other users in the system from viewing thosepersonal vocabulary words.

Many of these activities can be accomplished by using streamingdatabases in accordance with one example implementation. In oneparticular instance, this involves the use of streaming database feeder50. A streaming database continuously analyzes massive volumes ofdynamic information. Streaming database feeder 50 can create a usersub-stream for each user, where the tags could continuously be updatedfor that user. By writing a simple query, an individual can derive themost prevalent topics (e.g., based on a normalized count and time).

FIGS. 3 and 4 offer two distinct workflows for communication system 10.FIG. 3 addresses the corporate vocabulary formation, whereas FIG. 3addresses the personal vocabulary development. It should also be notedthat these illustrations are associated with more typical flowsinvolving simplistic documents propagating in a network (e.g., email,word processing documents, PDFs, etc.).

FIG. 3 is a simplified flowchart illustrating one example operationassociated with communication system 10. In this particular flow, atstep 110, end user 12 has written an email that includes the content“Optical Switching is a terrific technology.” This email message cantraverse the network and be received at a router (e.g., a largecorporate router, a switch, a switched port analyzer (SPAN) port, orsome type of virtual private network (VPN) network appliance). This isreflected by step 120. Network sensor 54 can be provisioned at such alocation in order to capture data and/or facilitate the identificationof content, as described herein.

In this particular example, FIFO element 56 may receive data in a rawformat at step 130. Text extraction module 58 may extract certain fieldsin order to identify a title, text, authorship, and a uniform resourcelocator (URL) associated with this particular document at step 140.[Note that as used herein in this Specification, the term ‘separate’ isused to encompass extraction, division, logical splitting, etc. of datasegments in a data flow. The term ‘tag’ as used herein in thisSpecification, is used to encompass any type of labeling, maintaining,identifying, etc. associated with data.] Note that for this particularinstance (where an email is being sent), the URL can have a blank field.The title may include a subject line, or an importance/priorityparameter, and the text field would have the quoted statement (i.e.,content), as written above. The document is then passed to blacklist 60,which searches (i.e., evaluates) the document to see if any blacklistedwords are found in the document (step 150). If any such blacklistedwords are present, the document is dropped. In one general sense, thereare two layers of privacy provided by blacklist 60 and whitelist 66,which are working together. Examples of blacklist words in a corporateenvironment may include ‘salary’, ‘merger’, etc., or possibly words thatmight offend public users, compromise privacy issues, implicateconfidential business transactions, etc. Note that the blacklist (muchlike the whitelist) can readily be configured by administrator 20 basedon particular user needs. The term ‘whitelist’ as used herein in thisSpecification is meant to connote any data sought to be targeted forinclusion into the resultant composite of words for administrator 20.Along similar reasoning, the term ‘blacklist’ as used herein is meant toinclude items that should not be included in the resultant composite ofwords.

Provided that the document in this instance is not dropped as a resultof the blacklist check, the document passes to document filter 62.Document filter 62 performs a quick check of the type of document thatis being evaluated at step 160. Again, this component is configurable asan administrator can readily identify certain types of documents asincluding more substantive or meaningful information (e.g., PDF or Wordprocessing documents, etc.). Along similar reasoning, some documents(such as JPEG pictures) may not offer a likelihood of findingsubstantive vocabulary (i.e., content) within the associated document.These more irrelevant documents may be (as a matter of practice) notevaluated for content and any such decision as to whether to ignorethese documents (e.g., JPEG pictures), or scrutinize them more carefullywould be left up to administrator 20.

In one example, noun phrase extractor module 64 includes a naturallanguage processing (NLP) component to assist it in its operations. Notethat a similar technology may exist in text extraction module 58 toassist it in its respective operations. One objective of noun phraseextractor module 64 is to extract meaningful objects from within textsuch that the content can be aggregated and further processed bycommunication system 10. In this example, noun phrase extractor module64 performs its job by extracting the terms “optical switching” and“technology.” This is illustrated by step 170.

Once this document has propagated through noun phrase extractor module64, the document passes to whitelist 66 at step 180. An administratormay wish to pick up certain whitelisted words in the content, as itpropagates through a network. The whitelist can be used on variousfields within communication system 10. In this particular example, thewhitelist is used to search the title and text fields. At this point,the document is sent to document splitter element 68. Note that thereare two documents being created from the original document. In oneinstance, document splitter element 68 can receive a document with fivefields including the concept field (at step 190), and perform severaloperations. First, it creates document #2 using the concept field indocument #1. Second, it removes the concept field from document #1.Third, it can remove all fields except the concept field from document#2. Fourth, it can send both document #1 and document #2 to clean topicsmodule 70.

It should be noted that noun phrase extractor module 64 operates bestwhen considering formal statements (e.g., using proper English).Colloquialisms or folksy speech is difficult to interpret from theperspective of any computer system. More informal documentation (e.g.,email) can be more problematic, because of the speech that dominatesthis forum.

Clean topics module 70 is configured to address some of thesespeech/grammar issues in several ways. In one example implementation,clean topics module 70 can receive two documents, as explained above. Itpasses document #1 without the concept field. For document #2, havingthe concept field, it can be configured to employ stop word removallogic at step 200. In this particular arrangement, the following stopwords can be removed: first name, last name, userid; functional stopword: A, an, the, etc.; email stop words: regards, thanks, dear, hi,etc.; non-alphabets: special characters, numbers; whitelist words: allwords found in a whitelist file configured by the administrator;administrator stop words: administrator rejected system words. Note thatthe operation of filtering functional stop words is different fromfiltering email (e.g., administrator stop words). For example, “Back OfAmerica” would not be processed into “Bank America.” Thus, stop wordsbetween two non-stop words would not necessarily be removed in certaininstances.

In addition, and in this particular example, the following rules can beapplied: Rule 1: Remove the entire noun phrase if a substring match isfound; Rule 2: Remove only the offending culprit; Rule 3: Remove theentire noun phrase if an exact match is found. Particular to thisexample, rules can be applied in the following order: Drop conceptfields containing non-alphabets (Rule 1); Drop concept fields containing(e.g., LDAP) entries (Rule 1); Drop concept fields containing email stopwords (Rule 1); Remove the functional stop word only if it is at eitherend of the concept field. Do not drop the words found in between, applyrule iteratively (Rule 2). Drop the concept field value if it is anexact match with the whitelist words (Rule 1). Drop the concept fieldvalue if it is an exact match with the administrator stop words (Rule1). Note that LDAP filtering can also occur during these activities. Forexample, if any proper names already in LDAP are identified, the filtercan just drop those terms.

Vocabulary feeder module 44 can receive the documents (e.g., on thecentral engine side) at step 210. Vocabulary feeder module 44 forwardsthe document without the concept field and, for the document with theconcept field, it sends it to streaming database feeder 50. In oneinstance, the streams are associated with storage technology, which isbased on a stream protocol (in contrast to a table format). In otherinstances, any other suitable technology can be employed to organize orto help process the incoming documents, content, etc. The streams can beupdated by vocabulary feeder module 44.

More specifically, the analytics approach of central engine 40 (in oneexample) involves having queries analyze streaming data. This strategyfor handling continuously flowing data is different from traditionalbusiness intelligence approaches of first accumulating data and thenrunning batch queries for reporting and analysis. Such an approachenables analysis of heterogeneous data regardless of whether the data isflowing, staged, etc. In addition, queries are continuous and constantlyrunning so new results are delivered when the downstream application canuse them. Data does not need to be stored or modified, so the system cankeep up with enormous data volumes. Thousands of concurrent queries canbe run continuously and simultaneously on a server architecture. Queriescan be run over both real-time and historical data. Incoming data can beoptionally persisted for replay, back-testing, drill-down, benchmarking,etc.

Returning to the flow of FIG. 3, vocabulary feeder module 44 can readthe concept field (e.g., created by the NLP module) and can feed thenoun phrases to the raw vocabulary stream (e.g., “raw_vocab_stream”file) at step 220. The vocabulary feeder mechanism can calculate theweight of each of the topics in the concept field by looking up a hashmap (initialized from a file) between the number of terms andcorresponding weight and, subsequently, feed the topic, calculatedweight, and timestamp into the raw vocabulary stream. The vocabularyfeeder's output can be configured to interface with the vocabularystream. The streams aggregate the topics into (for example) a weeklycollapsed vocabulary table (e.g., “weekly_collapsed_vocab_table” file),which could be updated during any suitable timeframe (e.g., hourly).This table serves as input to table write service element 48.

In regards to the periodic write service, a periodic service can invokethe write to administrator table service, as explained above. Thisservice can be configurable for the following: silent mode, hourly,daily, weekly, monthly. Hourly, daily, weekly, and monthly modesdesignate that the terms are suggested to an administrator on thespecified intervals. Hourly intervals could be used for testingpurposes. A silent mode offers a file based approach, where terms arewritten to a file, and do not make it to the administrator userinterface.

For table write service element 48, a service layer can read the weeklycollapsed vocabulary table for the top words and write to theadministrator user interface table. The administrator user interfacetable can represent the shared table between user-suggested vocabularyterms and the system suggested vocabulary terms. Administrator suggestinterface 38 can read the user-suggested vocabulary table(“userSuggestedVocabulary table”) to display the terms. This module cansuggest the top ‘n’ words to the administrator for adding to thevocabulary whitelist. Feedback loop module 36 may include applicationprogram interfaces (APIs) being provided to create a file from the tableof suggested vocabulary terms.

In this example, administrator suggest interface 38 reads the weeklycollapsed vocabulary table to display the terms at step 230. Thiselement also suggests the top (e.g., ‘n’) words to administrator 20 foraddition to the vocabulary whitelist. The administrator is provided auser interface to make decisions as to whether to add the term to thewhitelist, add it to the blacklist, or to ignore the terms. In oneexample implementation, the administrator does not suggest new stopwords. Only system suggested (or user suggested) stop words can berejected.

Feedback loop module 36 is coupled to administrator suggest interface38. In case the administrator chooses the “reject term” option, thesystem can add the term to the list of existing stop words and, further,propagate it to network sensor 54 to copy over to a file (e.g.,adminStopWords.txt). This is reflected by step 240. Networkcollaboration platform 32 can create a file from the table suggestedvocabulary terms (e.g., via commands including suggestedby=system, andstatus=rejected). This file can be a part of the force sync files thatcan be pushed to the network sensor/central engine (depending on wherethe stop words mechanism resides). At step 260, emerging vocabularytopics element 46 can look up emerging topics (e.g., within harvesteddocuments) and, systematically, add the emerging and top topics to thearchitecture for the administrator to consider. Both options can beprovided to administrator 20. The emerging topics can be similar to theexperience tags such that topics growing in prominence over a given timeinterval (e.g., a week) can be suggested to administrator 20.

FIG. 4 is a simplified flowchart illustrating one example operationassociated with communication system 10. In this particular flow, anemail is written from a first end user (John) to a second end user(Bill) at step 210. The email from John states, “Search engines aregood” and this is evaluated in the following ways. First, authorship isidentified and the email is searched for blacklisted and whitelistedwords at step 220. In essence, a number of text stripping operationsoccur for the received document (as outlined previously above in FIG.3). Second, the whitelisted words are received at LDAP feeder element 42at step 230. In one sense, the appropriate concept has been extractedfrom this email, where insignificant words have been effectivelystripped from the message and are not considered further.

At step 240, John is associated with the term “search engine” based onJohn authoring message and, in a similar fashion, Bill is associatedwith the term “search engine” based on him receiving this message. Notethat there is a different weight associated with John authoring thismessage, and Bill simply receiving it. At step 250, weighting module 55can be invoked in order to assign an intelligent weight based on thismessage propagating in the network. For example, as the author, John mayreceive a full point of weight associated with this particular subjectmatter (i.e., search engines). As the recipient, Bill may only receive ahalf point for this particular subject matter relationship (where Bill'spersonal vocabulary would include this term, but it would not carry thesame weight as this term being provided in John's personal vocabulary).

In addition, and as reflected by step 260, weighting module 55 maydetermine how common this word choice (i.e., “search engine”) is forthese particular end users. For example, if this were the first timethat John has written of search engines, it would be inappropriate tonecessarily tag this information and, subsequently, identify John as anexpert in the area of search engines. This email could be random,arbitrary, a mistake, or simply a rare occurrence. However, if over aperiod, this terminology relating to search engines becomes moreprominent (e.g., reaches a threshold), then John's personal vocabularymay be populated with this term.

In this particular example, several days after the initial email, Johnsends Bill a second email that includes a white paper associated withsearch engines, along with an accompanying video that is similarlytitled. This is reflected by step 270. Central engine 40 has theintelligence to understand that a higher weight should be accorded tothis subsequent transmission. Intuitively, the system can understandthat certain formats (White Papers, video presentations, etc.) are moremeaningful in terms of associating captured words with particularsubject areas. At step 280, weighting module 55 assigns this particulartransmission five points (three points for the White Paper and twopoints for the video presentation), where the five points would beallocated to John's personal vocabulary associated with search engines.In addition, Bill is also implicated by this exchange, where he wouldreceive a lesser point total for (passively) receiving this information.In this instance, and at step 290, Bill receives three points as being arecipient on this email. At step 300, the point totals are stored in anappropriate database on a per-user basis.

Additionally, over time, a social graph can be built based on theconnection between John and Bill and, in particular, in the context ofthe subject area of search engines. In one sense, the weight betweenthese two individuals can be bidirectional. A heavier weight is accordedto John based on these transmissions because he has been the dominantauthor in these exchanges. If Bill were to become more active and assumean authorship role in this relationship, then the weight metric couldshift to reflect his more proactive involvement. In one particularexample, a threshold of points is reached in order for Bill's personalvocabulary to include the term ‘search engine.’ This accounts for thescenario in which a bystander is simply receiving communications in apassive manner.

The architecture discussed herein can continue to amass and aggregatethese counts or points in order to build a personal vocabulary (e.g.,personal tags) for each individual end user. The personal vocabulary isintelligently partitioned such that each individual has his own group oftagged words to which he is associated. At the same time, a social graphcan continue to evolve as end users interact with each other aboutcertain subject areas.

In contrast to other systems that merely identify two individuals havingsome type of relationship, the architecture provided herein can offerthe context in which the relationship has occurred, along with aweighting that is associated with the relationship. For example, withrespect to the John/Bill relationship identified above, these twoindividuals may have their communications exclusively based on the topicof search engines. Bill could evaluate his own personal vocabulary andsee that John represents his logical connection to this particularsubject matter. He could also evaluate other less relevant connectionsbetween his colleagues having (in this particular example) a weakerrelationship associated with this particular subject matter.Additionally, an administrator (or an end user) can construct specificcommunities associated with individual subject matter areas. In oneexample, an administrator may see that John and Bill are activelyinvolved in the area of search engines. Several other end users can alsobe identified such that the administrator can form a small communitythat can effectively interact about issues in this subject area.

In another example, entire groups can be evaluated in order to identifycommon subject matter areas. For example, one group of end users may bepart of a particular business segment of a corporate entity. This firstgroup may be associated with switching technologies, whereas a secondgroup within the corporate entity may be part of a second businesssegment involving traffic management. By evaluating the vocabularyexchanged between these two groups, a common area of interest can beidentified. In this particular example, the personal vocabulary beingexchanged between the groups reveals a common interest in the subject ofdeep packet inspection.

Note that one use of the resulting data is to create a dynamic file foreach individual user that is tracked, or otherwise identified throughcommunication system 10. Other applications can involve identifyingcertain experts (or group of experts) in a given area. Other uses couldinvolve building categories or subject matter areas for a givencorporate entity. Note also that communication system 10 couldaccomplish the applications outlined herein in real time. Further, theassociation of the end users to particular subject matter areas can thenbe sent to networking sites, which could maintain individual profilesfor a given group of end users. This could involve platforms such asFacebook, LinkedIn, etc. The dynamic profile can be supported by thecontent identification operations associated with the tenderedarchitecture. In other applications, video, audio, and variousmultimedia files can be tagged by communication system 10 and associatedwith particular subject areas, or specific end user groups. In oneinstance, both the end user and the video file (or the audio file) canbe identified and logically bound together or linked.

Software for providing intelligent vocabulary building and argument mapfunctionality can be provided at various locations. In one exampleimplementation, this software is resident in a network element, such ascentral engine 40, NCP 32, and/or network sensor 54, or in anothernetwork element for which this capability is relegated. In otherexamples, this could involve combining central engine 40, NCP 32, and/ornetwork sensor 54 with an application server or a gateway, or someproprietary element, which could be provided in (or be proximate to)these identified network elements, or this could be provided in anyother device being used in a given network. In one specific instance,central engine 40 provides the personal vocabulary building featuresexplained herein, while network sensor 54 can be configured to offer theargument map activities detailed herein. In such an implementation,network sensor 54 can initially receive the data, employ its mediatagging functions, and then send the results to a text extractionmechanism, which can develop or otherwise process this information inorder to develop suitable argument maps.

In other embodiments, the argument map features may be providedexternally to network sensor 54, NCP 32, and/or central engine 40, orincluded in some other network device, or in a computer to achieve theseintended functionalities. As identified previously, a network elementcan include software to achieve the argument map and vocabulary buildingoperations, as outlined herein in this document. In certain exampleimplementations, the argument map and vocabulary building functionsoutlined herein may be implemented by logic encoded in one or moretangible media (e.g., embedded logic provided in an application specificintegrated circuit [ASIC], digital signal processor [DSP] instructions,software [potentially inclusive of object code and source code] to beexecuted by a processor, or other similar machine, etc.). In some ofthese instances, a memory element [as shown in some of the precedingFIGURES] can store data used for the operations described herein. Thisincludes the memory element being able to store software, logic, code,or processor instructions that are executed to carry out the activitiesdescribed in this Specification. A processor can execute any type ofinstructions associated with the data to achieve the operations detailedherein in this Specification. In one example, the processor [as shown insome of the preceding FIGURES] could transform an element or an article(e.g., data) from one state or thing to another state or thing. Inanother example, the activities outlined herein may be implemented withfixed logic or programmable logic (e.g., software/computer instructionsexecuted by a processor) and the elements identified herein could besome type of a programmable processor, programmable digital logic (e.g.,a field programmable gate array [FPGA], an erasable programmable readonly memory (EPROM), an electrically erasable programmable ROM (EEPROM))or an ASIC that includes digital logic, software, code, electronicinstructions, or any suitable combination thereof.

Any of these elements (e.g., the network elements, etc.) can includememory elements for storing information to be used in achieving theargument mapping and vocabulary building operations as outlined herein.Additionally, each of these devices may include a processor that canexecute software or an algorithm to perform the vocabulary building andargument mapping activities, as discussed in this Specification. Thesedevices may further keep information in any suitable memory element[random access memory (RAM), ROM, EPROM, EEPROM, ASIC, etc.], software,hardware, or in any other suitable component, device, element, or objectwhere appropriate and based on particular needs. Any of the memory itemsdiscussed herein should be construed as being encompassed within thebroad term ‘memory element.’ Similarly, any of the potential processingelements, modules, and machines described in this Specification shouldbe construed as being encompassed within the broad term ‘processor.’Each of the network elements can also include suitable interfaces forreceiving, transmitting, and/or otherwise communicating data orinformation in a network environment.

Note that with the examples provided herein, interaction may bedescribed in terms of two, three, four, or more network elements.However, this has been done for purposes of clarity and example only. Incertain cases, it may be easier to describe one or more of thefunctionalities of a given set of flows by only referencing a limitednumber of components or network elements. It should be appreciated thatcommunication system 10 of FIG. 1A (and its teachings) are readilyscalable. Communication system 10 can accommodate a large number ofcomponents, as well as more complicated or sophisticated arrangementsand configurations. Accordingly, the examples provided should not limitthe scope or inhibit the broad teachings of communication system 10 aspotentially applied to a myriad of other architectures.

It is also important to note that the steps described with reference tothe preceding FIGURES illustrate only some of the possible scenariosthat may be executed by, or within, communication system 10. Some ofthese steps may be deleted or removed where appropriate, or these stepsmay be modified or changed considerably without departing from the scopeof the discussed concepts. In addition, a number of these operationshave been described as being executed concurrently with, or in parallelto, one or more additional operations. However, the timing of theseoperations may be altered considerably. The preceding operational flowshave been offered for purposes of example and discussion. Substantialflexibility is provided by communication system 10 in that any suitablearrangements, chronologies, configurations, and timing mechanisms may beprovided without departing from the teachings of the discussed concepts.

1. A method, comprising: receiving network traffic associated with afirst user and a second user; evaluating keywords in the network trafficin order to identify a topic of discussion involving the first and thesecond users; determining a first sentiment associated with a first datasegment associated with the first user; determining a second sentimentassociated with a second data segment associated with the second user;and generating an argument map based on the first data sentiment and thesecond data sentiment.
 2. The method of claim 1, further comprising:converting a portion of the network traffic from a speech format to atext format in order to determine the first sentiment and the secondsentiment.
 3. The method of claim 1, wherein the argument map isdelivered to the first user and the second user as a result of adiscussion terminating between the first user and the second user. 4.The method of claim 1, wherein timestamps and Internet Protocol (IP)addresses associated with the first user and the second user areidentified, and wherein an interaction associated with the topic ofdiscussion is chronologically ordered before determining the firstsentiment and the second sentiment.
 5. The method of claim 1, whereinthe network traffic includes email data, media data, video data, andaudio data.
 6. The method of claim 1, wherein at least a portion of thenetwork traffic includes video data from a video conference that istagged.
 7. The method of claim 1, wherein email header information andsignatures are removed before determining the first sentiment and thesecond sentiment.
 8. Logic encoded in one or more tangible media thatincludes code for execution and when executed by a processor is operableto perform operations comprising: receiving network traffic associatedwith a first user and a second user; evaluating keywords in the networktraffic in order to identify a topic of discussion involving the firstand the second users; determining a first sentiment associated with afirst data segment associated with the first user; determining a secondsentiment associated with a second data segment associated with thesecond user; and generating an argument map based on the first datasentiment and the second data sentiment.
 9. The logic of claim 8, theoperations comprising: converting a portion of the network traffic froma speech format to a text format in order to determine the firstsentiment and the second sentiment.
 10. The logic of claim 8, whereinthe argument map is delivered to the first user and the second user as aresult of a discussion terminating between the first user and the seconduser.
 11. The logic of claim 8, wherein timestamps and Internet Protocol(IP) addresses associated with the first user and the second user areidentified, and wherein an interaction associated with the topic ofdiscussion is chronologically ordered before determining the firstsentiment and the second sentiment.
 12. The logic of claim 8, whereinthe network traffic includes email data, media data, video data, andaudio data.
 13. The logic of claim 8, wherein at least a portion of thenetwork traffic includes video data from a video conference that istagged.
 14. The logic of claim 8, wherein email header information andsignatures are removed before determining the first sentiment and thesecond sentiment.
 15. An apparatus, comprising: a memory elementconfigured to store data; a processor operable to execute instructionsassociated with the data; a network sensor configured to interface withthe memory element and the processor, wherein the apparatus isconfigured for: receiving network traffic associated with a first userand a second user; evaluating keywords in the network traffic in orderto identify a topic of discussion involving the first and the secondusers; determining a first sentiment associated with a first datasegment associated with the first user; determining a second sentimentassociated with a second data segment associated with the second user;and generating an argument map based on the first data sentiment and thesecond data sentiment.
 16. The apparatus of claim 15, the apparatusfurther configured for: converting a portion of the network traffic froma speech format to a text format in order to determine the firstsentiment and the second sentiment.
 17. The apparatus of claim 15,wherein the argument map is delivered to the first user and the seconduser as a result of a discussion terminating between the first user andthe second user.
 18. The apparatus of claim 15, wherein timestamps andInternet Protocol (IP) addresses associated with the first user and thesecond user are identified, and wherein an interaction associated withthe topic of discussion is chronologically ordered before determiningthe first sentiment and the second sentiment.
 19. The apparatus of claim15, wherein at least a portion of the network traffic includes videodata from a video conference that is tagged.
 20. The apparatus of claim15, wherein email header information and signatures are removed beforedetermining the first sentiment and the second sentiment.